Abstract
This article considers a misspecified linear regression model in which misspecification relates to the inclusion of some explanatory variables. Assuming the distribution of disturbances to be not necessarily normal, this paper investigates the efficiency properties of predictions arising from ordinary least squares and Stein-rule when the aim is to predict either the actual value or the mean value of the study variable.
Similar content being viewed by others
References
Dube, M., V.K. Srivastava, H. Toutenburg and P. Wijekoon (1991) SteinruleEstimator under Inclusion of Superfluous variables in Linear Regression Models” Communications in Statistics—Theory and Methods 20(7) 2009–2022.
Srivastava, V.K. and A.Chaturvedi (1986) “A Necessary and Sufficient condition for the Dominance of an Improved Family of Estimators in Linear Regression Models”, Economics Letters, 20, 345–349.
Srivastava, V.K. and M. Dube (1993). “Properties of the Ordinary least squares and Stein- rule Predictions in Linear Regression Models with Proxy Variables” Statistical Papers, 34, 27–41.
Trenkler, G. and H. Toutenberg (1992) “Pre—test Procedures and Forecasting in the Regression Model under Restrictions” Journal of Statistical Planning and Inference, 30, 250–1.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Srivastava, V.K., Dube, M. & Singh, V. Ordinary least squares and Stein-rule predictions in regression models under inclusion of some superfluous variables. Statistical Papers 37, 253–265 (1996). https://doi.org/10.1007/BF02926587
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF02926587